In this paper, to address the challenges with recurrent architectures and achieve high accuracy for sparse multichannel sEMG, we proposed the design of the \(\text{TC-HGR}\) , which is based on TCs and attention mechanism. More specifically, TCs use the dilated causal convolutions, which can increase the receptive field of the network and extract the temporal features of the sEMG signals. Moreover, by inspiring from [1]}, we divided the sEMG signals into patches to reduce the computational cost. The contributions of the paper can be summarized as follows:
